A recently proposed alternative for privacy-preserving training, without data outsourcing, is Federated Learning (FL) [1]}. FL has emerged as a promising approach to collaboratively train a model by exchanging model parameters with a central aggregator (or server), instead of the actual training data. However, parameter exchange may still leak a significant amount of private data [2]}. Several approaches have been proposed to overcome this leakage problem based on differential privacy (DP) [3]}, [4]}, MPC [5]}, [6]}, HE [7]}, etc. While DP-based learning aims to mitigate inference attacks, it significantly degrades model utility as the training of accurate models requires high privacy budgets  [8]}.
Cryptographic techniques provide improved privacy protection but remain too slow for practical use due to the extensive cryptographic operations. Hence, there arises a need for a secure, decentralized FL framework that protects user privacy, while allowing seamless training of ML models. This requires strong cryptographic protection of the intermediate model updates during the model aggregation and the final model weights.
